Exponentially faster fixed-parameter algorithms for high-multiplicity scheduling

TL;DR

This paper introduces exponentially faster fixed-parameter algorithms for high-multiplicity scheduling problems by exploiting a partition structure in N-fold integer programs, significantly improving computational efficiency over previous methods.

Contribution

The authors develop new algorithms for N-fold integer programs that leverage partition structures, leading to faster solutions for high-multiplicity scheduling problems.

Findings

Algorithms are exponentially faster than previous methods.

Structural parameters are small in many scheduling scenarios.

Applicable to various scheduling objectives like makespan and weighted completion time.

Abstract

We consider so-called $N$-fold integer programs (IPs) of the form $\max\{c^T x : Ax = b, \ell \leq x \leq u, x \in \mathbb Z^{nt}\}, where $A \in \mathbb Z^{(r+sn)\times nt} consists of $n$ arbitrary matrices $A^{(i)} \in \mathbb Z^{r\times t}$ on a horizontal, and $n$ arbitrary matrices $B^{(j)} \in \mathbb Z^{s\times t} on a diagonal line. Several recent works design fixed-parameter algorithms for $N$-fold IPs by taking as parameters the numbers of rows and columns of the $A$- and $B$-matrices, together with the largest absolute value $\Delta$ over their entries. These advances provide fast algorithms for several well-studied combinatorial optimization problems on strings, on graphs, and in machine scheduling. In this work, we extend this research by proposing algorithms that additionally harness a partition structure of submatrices $A^{(i)}$ and $B^{(j)}$, where row indices of non-zero entries do not overlap between any two sets in the partition. Our main result is an algorithm for solving any $N$-fold IP in time $nt log(nt)L^2(S_A)^{O(r+s)}(p_Ap_B\Delta)^{O(rp_Ap_B+sp_Ap_B)}$, where $p_A$ and $p_B$ are the size of the largest set in such a partition of $A^{(i)}$ and $B^{(j)}$, respectively, $S_A$ is the number of parts in the partition of $A = (A^{(1)},..., A^{(n)}), and $L = (log(||u - \ell||_\infty)\cdot (log(max_{x:\ell \leq x \leq u} |c^Tx|))$ is a measure of the input. We show that these new structural parameters are naturally small in high-multiplicity scheduling problems, such as makespan minimization on related and unrelated machines, with and without release times, the Santa Claus objective, and the weighted sum of completion times. In essence, we obtain algorithms that are exponentially faster than previous works by Knop et al. (ESA 2017) and Eisenbrand et al./Kouteck{\'y} et al. (ICALP 2018) in terms of the number of job types.